{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.stats import gamma, norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu_0 = 200\n",
    "zeta_0 = 1\n",
    "alpha_0 = 1\n",
    "beta_0 = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gen_lambda_dist(alpha, beta):\n",
    "    return gamma(alpha, scale=1/beta)\n",
    "\n",
    "def gen_mu_dist(mu_mean, zeta, lmd):\n",
    "    return norm(loc=mu_mean, scale=np.sqrt(1/(zeta*lmd)))\n",
    "\n",
    "def draw(pdf, range_min, range_max, step):\n",
    "    xs = np.arange(range_min, range_max, step)\n",
    "    ys = [pdf.pdf(x) for x in xs]\n",
    "    plt.plot(xs, ys)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lambda_dist = gen_lambda_dist(alpha_0, beta_0) \n",
    "draw(lambda_dist, 0, 5, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lmd = lambda_dist.rvs()                                            ###gaussgamma1mu\n",
    "mu_dist = gen_mu_dist(mu_0, zeta_0, lmd)\n",
    "draw(mu_dist, 180, 220, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "data  = pd.read_csv(\"sensor_data_200.txt\", delimiter=\" \", \n",
    "                    header=None, names = (\"date\",\"time\",\"ir\",\"lidar\"))\n",
    "lidar = data[\"lidar\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[214 207 203 210 216]\n",
      "平均:  210.0\n",
      "標準偏差:  5.244044240850758\n"
     ]
    }
   ],
   "source": [
    "samples = lidar.sample(5)\n",
    "print(samples.values)\n",
    "print(\"平均: \", samples.mean())\n",
    "print(\"標準偏差: \", samples.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[207.14285714285714, 6, 3.5, 1582.5102040816273]\n"
     ]
    }
   ],
   "source": [
    "N = len(samples)\n",
    "mu_N = 1.0/(N+beta_0)*sum(samples) + beta_0/(N+beta_0)*mu_0\n",
    "zeta_N = N + zeta_0\n",
    "alpha_N = N/2 + alpha_0\n",
    "beta_N = 0.5*(sum([z**2 for z in samples]) + zeta_0*(mu_0**2) - zeta_N*(mu_N**2)) + beta_0\n",
    "print([mu_N, zeta_N, alpha_N, beta_N])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lambda_dist = gen_lambda_dist(alpha_N, beta_N) \n",
    "draw(lambda_dist, 0, 5, 0.01)\n",
    "draw(lambda_dist, 0, 0.01, 0.00001) #範囲・縮尺を変えたもの"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "平均:  207.14285714285714\n",
      "標準偏差:  18.49258003015676\n"
     ]
    }
   ],
   "source": [
    "lmd = lambda_dist.rvs()  ###gaussgamma1postmu\n",
    "mu_dist = gen_mu_dist(mu_N, zeta_N, lmd)\n",
    "draw(mu_dist, 180, 220, 0.1)\n",
    "\n",
    "print(\"平均: \", mu_dist.mean())\n",
    "print(\"標準偏差: \", np.sqrt(1/lmd))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
